R Tutorial with Bayesian Statistics Using OpenBUGS

520 Pages·2013·3.5 MB·English

by  Chi Yau

R Tutorial
with Bayesian Statistics
Using OpenBUGS
By Chi Yau
Copyright © 2012-2015 by Chi Yau
All rights reserved.

Reproduction or translation of any part of
this work beyond that permitted by Sections
107 and 108 of the 1976 United States Copyright
Act without the permission of the copyright
holder is unlawful.
Preface
This text provides R tutorials on statistics including hypothesis testing, ANOVA
and linear regressions. It fulfills popular demands by users of r-tutor.com for
exercise solutions and offline access.
Part III of the text is about Bayesian statistics. It begins with closed analytic
solutions and basic BUGS models for simple examples. Then it covers
OpenBUGS for Bayesian ANOVA and regression analysis. Finally, it shows how
to build more complex Bayesian models and demonstrates CODA for Markov
Chain Monte Carlo (MCMC) convergence.
The last part of this text discusses advanced GPU computing in R using the
RPUDPLUS package. Topics include hierarchical clustering, Kendall's tau,
support vector machines and Bayesian classification. It illustrates the importance
of High Performance Computing (HPC) in the future of statistics. The text
concludes with an extra section on hierarchical multinomial logit model for
marketing research.
This eBook is published in Amazon Kindle format. It is a great tool for self-study.
The R source code is available for download in r-tutor.com.
I am grateful to users who sent me donations in the past. It provides me
encouragement and support in ways unimaginable. I would also like to thank users
who alert me of errors. I really appreciate it if you can email me further feedback
to [email protected].

Chi Yau
Palo Alto, California
April, 2014
TABLE OF CONTENTS
Preface
Full Table of Contents
PART I
R Introduction
CHAPTER 1 Basic Data Types
CHAPTER 2 Vector
CHAPTER 3 Matrix
CHAPTER 4 List
CHAPTER 5 Data Frame
PART II
Elementary Statistics with R
CHAPTER 6 Qualitative Data
CHAPTER 7 Quantitative Data
CHAPTER 8 Numerical Measures
CHAPTER 9 Probability Distributions
CHAPTER 10 Interval Estimation
CHAPTER 11 Hypothesis Testing
CHAPTER 12 Type II Errors
CHAPTER 13 Inference About Two Populations
CHAPTER 14 Goodness of Fit
CHAPTER 15 Analysis of Variance
CHAPTER 16 Non-parametric Methods
CHAPTER 17 Simple Linear Regression
CHAPTER 18 Multiple Linear Regression
CHAPTER 19 Logistic Regression
PART III
Bayesian Statistics Using OpenBUGS
CHAPTER 20 Bayesian Finite Inference
CHAPTER 21 Bayesian Binomial Inference
CHAPTER 22 Bayesian Poisson Inference
CHAPTER 23 Bayesian Inference for Normal Mean
CHAPTER 24 Bayesian Inference for Two Populations
CHAPTER 25 Bayesian Analysis of Variance
CHAPTER 26 Bayesian Simple Linear Regression
CHAPTER 27 Bayesian Multiple Linear Regression
CHAPTER 28 Bayesian Logistic Regression
CHAPTER 29 Hierarchical Models
PART IV
GPU Computing with R
CHAPTER 30 Statistical Computing on GPU
Appendix
A. Installing GPU Packages
B. Installing OpenBUGS
References
Full Table of Contents
Preface